Version 1.0

Nucleoside quantification in the plasma of G2019S LRRK2 knockin mice

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Ma, Yue;Moore, Darren

Description

This Zenodo deposit contains a publicly available description of the Dataset:Title: "Nucleoside quantification in the plasma of G2019S LRRK2 knockin mice".Description: G2019S LRRK2 knockin mice and their wild-type littermates (3-5 months old) were anesthetized with an isoflurane vaporizer, and whole blood was collected from cardiac puncture. 50 μL plasma from each mouse was split and processed using the Bligh-Dyer method. Absolute nucleoside quantitation was accomplished by running an external calibration curve with the samples. The stock mix contained Deoxyadenosine (dA), Deoxycytidine (dC), Deoxyguanosine (dG), Deoxythymidine (dT), and Deoxyuridine (dU). Targeted metabolomics peak picking and integration were conducted in Skyline (v25.1) using accurate mass MS1, MS2 fragmentation pattern matching, and retention time derived from analytical standards run through each chromatography method. Raw data files for all samples of a given experiment were imported and metabolite peaks were auto-integrated based standard verified m/z, precursor adducts, and retention times for all metabolites of interest. Each experimental group contained biological replicates (n =8-9 per group).This dataset is made available to researchers via the ASAP CRN Cloud: cloud.parkinsonsroadmap.org. Instructions for how to request access can be found in the User Manual.This research was funded by the Aligning Science Across Parkinson's Collaborative Research Network (ASAP CRN), through the Michael J. Fox Foundation for Parkinson's Research (MJFF).This Zenodo deposit was created by the ASAP CRN Cloud staff on behalf of the dataset authors. It provides a citable reference for a CRN Cloud Dataset

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Mentions (0)

Metrics

Dataset Index

0.4

FAIR Score

85%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Zenodo

License

Creative Commons Attribution 4.0 International

Assigned Domain

Subfield

Immunology

Field

Immunology and Microbiology

Domain

Life Sciences

Confidence Score

43%

Source

Scholar Data Model

Normalization Factors

FT

65.38

CTw

1.00

MTw

1.00